OpenRaiser/Intern-Atlas huggingface dataset:
Resource production of written forms of Sign Languages by a user-centered editor, SWift (SignWriting improved fast transcriber)
Venue: arXiv cs.CL 2019
Year: 2019

The SignWriting improved fast transcriber (SWift), presented in this paper, is an advanced editor for computer-aided writing and transcribing of any Sign Language (SL) using SignWriting (SW). The application is an editor which allows composing and saving desired signs using the SW elementary components, called "glyphs". These make up a sort of alphabet, which does not depend on the national Sign Language and which codes the basic components of any sign. The user is guided through a fully-automated procedure, making the composition process fast and intuitive. SWift pursues the goal of helping to break down the "electronic barriers" that keep deaf people away from the web, and at the same time to support linguistic research about Sign Languages features. For this reason it has been designed with a special attention to deaf user needs, and to general usability issues. The editor has been developed in a modular way, so it can be integrated everywhere the use of SW as an alternative to written "verbal" language may be advisable.
Insider threat modeling: An adversarial risk analysis approach
Venue: arXiv cs.CR 2019
Year: 2019

Insider threats entail major security issues in geopolitics, cyber risk management and business organization. The game theoretic models proposed so far do not take into account some important factors such as the organisational culture and whether the attacker was detected or not. They also fail to model the defensive mechanisms already put in place by an organisation to mitigate an insider attack. We propose two new models which incorporate these settings and hence are more realistic. %Most earlier work in the field has focused on %standard game theoretic approaches to find the solutions. We use the adversarial risk analysis (ARA) approach to find the solution to our models. ARA does not assume common knowledge and solves the problem from the point of view of one of the players, taking into account their knowledge and uncertainties regarding the choices available to them, to their adversaries, the possible outcomes, their utilities and their opponents' utilities. Our models and the ARA solutions are general and can be applied to most insider threat scenarios. A data security example illustrates the discussion.
Four presumed gaps in the software engineering research community's knowledge
Venue: arXiv cs.SE 2019
Year: 2019

Background: The state of the art in software engineering consists of a myriad of contributions and the gaps between them; it is difficult to characterize. Questions: In order to help understanding the state of the art, can we identify gaps in our knowledge that are at a very general, widely relevant level? Which research directions do these gaps suggest? Method: 54 expert interviews with senior members of the ICSE community, evaluated qualitatively using elements of Grounded Theory Methodology. Results: Our understanding of complexity, of good-enoughness, and of developers' strengths is underdeveloped. Some other relevant factors' relevance is apparently not clear. Software engineering is not yet an evidence-based discipline. Conclusion: More software engineering research should concern itself with emergence phenomena, with how engineering tradeoffs are made, with the assumptions underlying research works, and with creating certain taxonomies. Such work would also allow software engineering to become more evidence-based.
Anaphora Resolution in Dialogue Systems for South Asian Languages
Venue: arXiv cs.CL 2019
Year: 2019

Anaphora resolution is a challenging task which has been the interest of NLP researchers for a long time. Traditional resolution techniques like eliminative constraints and weighted preferences were successful in many languages. However, they are ineffective in free word order languages like most SouthAsian languages.Heuristic and rule-based techniques were typical in these languages, which are constrained to context and domain.In this paper, we venture a new strategy us-ing neural networks for resolving anaphora in human-human dialogues. The architecture chiefly consists of three components, a shallow parser for extracting features, a feature vector generator which produces the word embed-dings, and a neural network model which will predict the antecedent mention of an anaphora.The system has been trained and tested on Telugu conversation corpus we generated. Given the advantage of the semantic information in word embeddings and appending actor, gender, number, person and part of plural features the model has reached an F1-score of 86.
SparseTrain:Leveraging Dynamic Sparsity in Training DNNs on General-Purpose SIMD Processors
Venue: arXiv cs.LG 2019
Year: 2019

Our community has greatly improved the efficiency of deep learning applications, including by exploiting sparsity in inputs. Most of that work, though, is for inference, where weight sparsity is known statically, and/or for specialized hardware. We propose a scheme to leverage dynamic sparsity during training. In particular, we exploit zeros introduced by the ReLU activation function to both feature maps and their gradients. This is challenging because the sparsity degree is moderate and the locations of zeros change over time. We also rely purely on software. We identify zeros in a dense data representation without transforming the data and performs conventional vectorized computation. Variations of the scheme are applicable to all major components of training: forward propagation, backward propagation by inputs, and backward propagation by weights. Our method significantly outperforms a highly-optimized dense direct convolution on several popular deep neural networks. At realistic sparsity, we speed up the training of the non-initial convolutional layers in VGG16, ResNet-34, ResNet-50, and Fixup ResNet-50 by 2.19x, 1.37x, 1.31x, and 1.51x respectively on an Intel Skylake-X CPU.
Construction of a Validated Virtual Embodiment Questionnaire
Venue: arXiv cs.HC 2019
Year: 2019

User embodiment is important for many virtual reality (VR) applications, for example, in the context of social interaction, therapy, training, or entertainment. However, there is no validated instrument to empirically measure the perception of embodiment, necessary to reliably evaluate this important quality of user experience (UX). To assess components of virtual embodiment in a valid, reliable, and consistent fashion, we develped a Virtual Embodiment Questionnaire (VEQ). We reviewed previous literature to identify applicable constructs and items, and performed a confirmatory factor analysis (CFA) on the data from three experiments (N = 196). Each experiment modified a distinct simulation property, namely, the level of immersion, the level of personalization, and the level of behavioral realism. The analysis confirmed three factors: (1) ownership of a virtual body, (2) agency over a virtual body, and (3) change in the perceived body schema. A fourth study (N = 22) further confirmed the reliability and validity of the scale and investigated the impacts of latency jitter of avatar movements presented in the simulation compared to linear latencies and a baseline. We present the final scale and further insights from the studies regarding related constructs.
Design and Experiments with a Robot-Driven Underwater Holographic Microscope for Low-Cost In Situ Particle Measurements
Venue: arXiv cs.RO 2019
Year: 2019

Microscopic analysis of micro particles in situ in diverse water environments is necessary for monitoring water quality and localizing contamination sources. Conventional sensors such as optical microscopes and fluorometers often require complex sample preparation, are restricted to small sample volumes, and are unable to simultaneously capture all pertinent details of a sample such as particle size, shape, concentration, and three dimensional motion. In this article we propose a novel and cost-effective robotic system for mobile microscopic analysis of particles in situ at various depths which are fully controlled by the robot system itself. A miniature underwater digital in-line holographic microscope (DIHM) performs high resolution imaging of microparticles (e.g., algae cells, plastic debris, sediments) while movement allows measurement of particle distributions covering a large area of water. The main contribution of this work is the creation of a low-cost, comprehensive, and small underwater robotic holographic microscope that can assist in a variety of tasks in environmental monitoring and overall assessment of water quality such as contaminant detection and localization. The resulting system provides some unique capabilities such as expanded and systematic coverage of large bodies of water at a low cost. Several challenges such as the trade-off between image quality and cost are addressed to satisfy the aforementioned goals.
SWAG: Item Recommendations using Convolutions on Weighted Graphs
Venue: arXiv cs.IR 2019
Year: 2019

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SWAG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weighted graphs to generate embeddings for nodes (items) that incorporate both graph structure as well as node feature information such as item-descriptions and item-images. The three important SWAG operations that enable us to efficiently generate node embeddings based on graph structures are (a) Sampling of graph to homogeneous structure, (b) Weighting the sampling, walks and convolution operations, and (c) using AGgregation functions for generating convolutions. The work is an adaptation of graphSAGE over weighted graphs. We deploy SWAG at Target and train it on a graph of more than 500K products sold online with over 50M edges. Offline and online evaluations reveal the benefit of using a graph-based approach and the benefits of weighing to produce high quality embeddings and product recommendations.
Improving N-gram Language Models with Pre-trained Deep Transformer
Venue: arXiv cs.CL 2019
Year: 2019

Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference. In this paper, we demonstrate that n-gram LM can be improved by neural LMs through a text generation based data augmentation method. In contrast to previous approaches, we employ a large-scale general domain pre-training followed by in-domain fine-tuning strategy to construct deep Transformer based neural LMs. Large amount of in-domain text data is generated with the well trained deep Transformer to construct new n-gram LMs, which are then interpolated with baseline n-gram systems. Empirical studies on different speech recognition tasks show that the proposed approach can effectively improve recognition accuracy. In particular, our proposed approach brings significant relative word error rate reduction up to 6.0% for domains with limited in-domain data.
Learning a Representation with the Block-Diagonal Structure for Pattern Classification
Venue: arXiv cs.CV 2019
Year: 2019

Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method.
Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations
Venue: arXiv cs.DC 2019
Year: 2019

Numerical simulations on mobile devices are an important tool for engineers and decision makers in the field. However, providing simulation results on mobile devices is challenging due to the complexity of the simulation, requiring remote server resources and distributed mobile computation. The additional large size of multi-dimensional simulation results leads to the insufficient performance of existing approaches, especially when the bandwidth of wireless communication is scarce. In this article, we present an optimized novel approach utilizing surrogate models and data assimilation techniques to reduce the communication overhead. Evaluations show that our approach is up to $6.5$ times faster than streaming results from the server while still meeting required quality constraints.
Oscillator Circuit for Spike Neural Network with Sigmoid Like Activation Function and Firing Rate Coding
Venue: arXiv cs.ET 2019
Year: 2019

The study presents an oscillator circuit for a spike neural network with the possibility of firing rate coding and sigmoid-like activation function. The circuit contains a switching element with an S-shaped current-voltage characteristic and two capacitors; one of the capacitors is shunted by a control resistor. The circuit is characterised by a strong dependence of the frequency of relaxation oscillations on the magnitude of the control resistor. The dependence has a sigmoid-like form and we present an analytical method for dependence calculation. Finally, we describe the concept of the spike neural network architecture with firing rate coding based on the presented circuit for creating neuromorphic devices and artificial intelligence.
No Need for Recovery: A Simple Two-Step Byzantine Consensus
Venue: arXiv cs.DC 2019
Year: 2019

In this paper, we give a deterministic two-step Byzantine consensus protocol that achieves safety and liveness. A two-step Byzantine consensus protocol only needs two communication steps to commit in the absence of faults. Most two-step Byzantine consensus protocols exploit optimism and require a recovery protocol in the presence of faults. In this paper, we give a simple two-step Byzantine consensus protocol that does not need a recovery protocol.
Joint Parsing and Generation for Abstractive Summarization
Venue: arXiv cs.CL 2019
Year: 2019

Sentences produced by abstractive summarization systems can be ungrammatical and fail to preserve the original meanings, despite being locally fluent. In this paper we propose to remedy this problem by jointly generating a sentence and its syntactic dependency parse while performing abstraction. If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged. The proposed method thus holds promise for producing grammatical sentences and encouraging the summary to stay true-to-original. Our contributions of this work are twofold. First, we present a novel neural architecture for abstractive summarization that combines a sequential decoder with a tree-based decoder in a synchronized manner to generate a summary sentence and its syntactic parse. Secondly, we describe a novel human evaluation protocol to assess if, and to what extent, a summary remains true to its original meanings. We evaluate our method on a number of summarization datasets and demonstrate competitive results against strong baselines.
When is ACL's Deadline? A Scientific Conversational Agent
Venue: arXiv cs.CL 2019
Year: 2019

Our conversational agent UKP-ATHENA assists NLP researchers in finding and exploring scientific literature, identifying relevant authors, planning or post-processing conference visits, and preparing paper submissions using a unified interface based on natural language inputs and responses. UKP-ATHENA enables new access paths to our swiftly evolving research area with its massive amounts of scientific information and high turnaround times. UKP-ATHENA's responses connect information from multiple heterogeneous sources which researchers currently have to explore manually one after another. Unlike a search engine, UKP-ATHENA maintains the context of a conversation to allow for efficient information access on papers, researchers, and conferences. Our architecture consists of multiple components with reference implementations that can be easily extended by new skills and domains. Our user-based evaluation shows that UKP-ATHENA already responds 45% of different formulations of defined intents with 37% information coverage rate.
Anisotropic multicomponent topology optimization for additive manufacturing with build orientation design and stress-constrained interfaces
Venue: arXiv cs.CE 2019
Year: 2019

This paper presents a multicomponent topology optimization method for designing structures assembled from additively-manufactured components, considering anisotropic material behavior for each component due to its build orientation, distinct material behavior and stress constraint at component interfaces (i.e., joints). Based upon the multicomponent topology optimization (MTO) framework, the simultaneous optimization of structural topology, its partitioning, and the build orientations of each component is achieved, which maximizes an assembly-level structural stiffness performance subject to maximum stress constraints at component interfaces. The build orientations of each component are modeled by its orientation tensor that avoids numerical instability experienced by the conventional angular representation. A new joint model is introduced at component interfaces, which enables the identification of the interface location, the specification of a distinct material tensor, and imposing maximum stress constraints during optimization. Both 2D and 3D numerical examples are presented to illustrate the effect of the build orientation anisotropy and the component interface behavior on the resulting multicomponent assemblies.
SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
Venue: arXiv cs.CL 2019
Year: 2019

In response to the continuing research interest in computational semantic analysis, we have proposed a new task for SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of nominals. The task is designed to compare different approaches to the problem and to provide a standard testbed for future research. In this paper, we define the task, describe the creation of the datasets, and discuss the results of the participating 28 systems submitted by 10 teams.
Negligible Cooperation: Contrasting the Maximal- and Average-Error Cases
Venue: arXiv cs.IT 2019
Year: 2019

In communication networks, cooperative strategies are coding schemes where network nodes work together to improve network performance metrics such as the total rate delivered across the network. This work studies encoder cooperation in the setting of a discrete multiple access channel (MAC) with two encoders and a single decoder. A network node, here called the cooperation facilitator (CF), that is connected to both encoders via rate-limited links, enables the cooperation strategy. Previous work by the authors presents two classes of MACs: (i) one class where the average-error sum-capacity has an infinite derivative in the limit where CF output link capacities approach zero, and (ii) a second class of MACs where the maximal-error sum-capacity is not continuous at the point where the output link capacities of the CF equal zero. This work contrasts the power of the CF in the maximal- and average-error cases, showing that a constant number of bits communicated over the CF output link can yield a positive gain in the maximal-error sum-capacity, while a far greater number of bits, even numbers that grow sublinearly in the blocklength, can never yield a non-negligible gain in the average-error sum-capacity.
Real-time Analysis of Privacy-(un)aware IoT Applications
Venue: arXiv cs.CR 2019
Year: 2019

Users trust IoT apps to control and automate their smart devices. These apps necessarily have access to sensitive data to implement their functionality. However, users lack visibility into how their sensitive data is used (or leaked), and they often blindly trust the app developers. In this paper, we present IoTWatcH, a novel dynamic analysis tool that uncovers the privacy risks of IoT apps in real-time. We designed and built IoTWatcH based on an IoT privacy survey that considers the privacy needs of IoT users. IoTWatcH provides users with a simple interface to specify their privacy preferences with an IoT app. Then, in runtime, it analyzes both the data that is sent out of the IoT app and its recipients using Natural Language Processing (NLP) techniques. Moreover, IoTWatcH informs the users with its findings to make them aware of the privacy risks with the IoT app. We implemented IoTWatcH on real IoT applications. Specifically, we analyzed 540 IoT apps to train the NLP model and evaluate its effectiveness. IoTWatcH successfully classifies IoT app data sent to external parties to correct privacy labels with an average accuracy of 94.25%, and flags IoT apps that leak privacy data to unauthorized parties. Finally, IoTWatcH yields minimal overhead to an IoT app's execution, on average 105 ms additional latency.
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context
Venue: arXiv cs.CL 2019
Year: 2019

Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context. In task-oriented dialogs, this property leads to different valid dialog policies towards task completion. However, none of the existing task-oriented dialog generation approaches takes this property into account. We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. Specifically, we first use dialog states to summarize the dialog history, and then discover all possible mappings from every dialog state to its different valid system actions. During dialog system training, we enable the current dialog state to map to all valid system actions discovered in the previous process to create additional state-action pairs. By incorporating these additional pairs, the dialog policy learns a balanced action distribution, which further guides the dialog model to generate diverse responses. Experimental results show that the proposed framework consistently improves dialog policy diversity, and results in improved response diversity and appropriateness. Our model obtains state-of-the-art results on MultiWOZ.
Causality for Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.